AI Landscape
A high-level map of the AI ecosystem. Each node links to the relevant section of the site.
Foundation models, reasoning models, open and closed weights. Capabilities, context windows and known limitations.
The hardware, cloud platforms and tooling that models run on: GPUs, training clusters, inference serving, orchestration.
How organisations deploy AI: coding assistants, agents, RAG systems, enterprise integration patterns.
Model developers, semiconductor companies, cloud providers, robotics and research organisations.
Breakthroughs, papers and emerging techniques. Transformers, attention, alignment, reasoning and evaluation.
The EU AI Act, NIST AI RMF, ISO/IEC 42001. Obligations, timelines and risk classification.
OWASP Top 10 for LLM Applications 2026, the AI Security Verification Standard, prompt injection and agency risks.
Context window economics, inference cost, training cost, pricing models and the economics of open vs closed.
A chronological record from Turing to the present: research milestones, model releases, regulation and industry events.
Core terms defined once: transformer, embedding, hallucination, alignment, quantisation and more.
Structured paths from beginner to advanced, with prerequisites, outcomes and hands-on projects.
Early indicators, emerging trends and weak signals worth watching.